‘’A comparison of herd behaviour in
developing and developed financial markets.’’
Author: Zahra Bhatti
Supervisor: Charlotte Carruthers
Word count: 7360
Table of Contents
Chapter 1: Introduction
1.1 Background
1.2 Purpose
Chapter 2: Literature Review
2.1 Theoretical background
2.2 Theoretical and empirical challenges to efficient market hypothesis
2.3 Herd Behaviour in financial markets
2.4 Investor sentiments for herding
Chapter 3: Methodology
3.1 Countries in question
3.2 Method used to measure herd behaviour in studies
3.3 Limitations
Chapter 4: Results
Chapter 5: Discussion
5.1 Which countries exhibited herding the most?
5.2 Outcome of discussion
Chapter 6: Conclusion
6.1 Conclusion of literature review and results
6.2 Contribution
6.3 Suggestions for future research.
Acknowledgements
References
Chapter 1: Introduction
1.1 Background
It is evident that in financial markets today, there is an abundance of stock market fluctuations
and unexplained anomalies. For decades this has been an interest for investors, researchers
and relevant regulatory bodies. Within this time, the famous Efficient Market Hypothesis
(EMH) was formulated by Fama (1965). The main idea assumes the existence of markets
behaving in an efficient and rational manner whereby all information is reflected in stock
market prices and readily available on the market. Based on this, markets can be weak, semistrong and strongly efficient. Essentially the EMH suggests that it is not possible to overcome
the market regularly. However, the extent to which markets are truly efficient and whether
market participants are indeed rational became an issue resulting in a body of empirical
studies challenging the ideas of EMH. New fields have emerged in to order to question these
doubts. One of the prominent fields is behavioural finance, which aims to financial crashes,
market anomalies, stock market bubbles and biases effecting the financial markets (Shiller,
2003). Behavioural economists who investigate into behavioural finance theories, specify that
there are particular hurdles to market efficient which must be examined, for example limits
to arbitrage (Thaler., 2003) and economic behavioural factors such as loss aversion amongst
investors (Haigh and List, 2005). Another aspect which researchers find that deviates markets
to become inefficient, is herd behaviour on financial markets. Initially, herding is an
occurrence derived from animals, which narrates the behavioural pattern of assembling into
groups in order to pursue conservation from predators and to hunt. This concept was then
applied in an economic context to humans by Keynes (1936). As the appeal of the influence
of behavioural factors grew in financial markets, herding became an interest in this financial
field. Bikchandani and Sharma (2001) define herding in a financial context, whereby investors
ignore their own private information and mimic the actions of other well-established
investors. Applying this definition, this paper aims to obtain more evidence on the compound
issue of herding. This paper will carry out an investigation into the extent of herding in both
developing and developed markets.
1.2 Purpose
The basis of this research is to explore whether herd behaviour exists in various financial
markets. The findings into this area, expands upon previous studies which investigate herd
behaviour in many diverse markets. The aim of this research is to contribute to these studies,
and explore whether the extent of herding is more significant in developing countries
compared to their developed equivalents. The interesting factor of examining developing
markets, is that in theory they should be less efficient than developed markets. The
developing countries which have been chosen for research are: India, China and Russia
whereas the developed countries are: UK,US and Portugal. The main reasons for choosing
these set of countries, is that they differ greatly in terms of economy and culture therefore
the results of these studies should indicate various levels of herding. Furthermore, each of
these countries have exhibited market anomalies such as the Russian Crisis, Subprime Crisis,
Dot Com Crash and European Sovereign Crisis. Furthermore, Russia, China and India all have
relatively poor investor protection and information asymmetry (Corcoran., 2013). In order to
examine the research question, three supporting objectives are outlined and then the results
are studied.
.
1. Which countries exhibited herding the most, during which time period, for example
before, after or during financial crisis.
2. Explore whether time-varying market movements have an effect on herd behaviour,
for example stock markets moving up or down.
3. Critically review strengths and limitations of existing studies and incentives for
investors to converge or diverge behaviourally.
The paper is organised into five chapters. Chapter two provides an insight into seminal theory
and the challenges towards it, as well as answering objective number three in regards to
investor sentiment. Chapter three will outline the methodology used in this paper and in
previous studies in order to create a regression model, derived from results of previous
studies. Chapter four will present the model created with the results obtained. Chapter five
provides an insightful discussion as well as an analysis of which countries exhibited herd
behaviour the most, whilst section six ends with a conclusion and suggestions for further
research in the field.
Chapter 2: Literature Review
The following chapter will aim to review the literature and its empirical evidence. The first
section will outline the underlying theory needed in order to fully grasp the concept of
herding. This will then be followed by the factors which challenge the theory. Furthermore,
the chapter will also discuss investor sentiment towards herding as well as the behavioural
factors which lead them to converge behaviourally.
2.1 Theoretical Background
Efficient Market Hypothesis (EMH) has long dominated the economic profession, this theory
introduced by Fama (1970) suggests that financial markets are competitive and composed of
rational agents where price converges to its fundamental value. EMH has been regarded as a
principal element to modern financial theory. There are three forms of market efficiency;
weak, semi-strong and strong. The weak form suggests that all prices reflect historical
information. Fama (1965) states in his article ‘Random walks in stock-market prices’ that
actual prices of individual securities already reflect the effects of information which has
already occurred. The semi-strong form suggests that prices immediately reflect all publicly
available information. Research contributed by Fisher et al (1969) found that important global
affairs were incorporated and reflected into share prices immediately, at the time of public
announcement. Lastly, the strong form of EMH suggests that all private information is leaked
and incorporated into the market, thus preventing investors from making profit via insider
information.
Furthermore, the theory is based upon three arguments about investor rationality. Firstly
investors are presumed to be rational and thus securities are priced rationally within the
market. Although, it may be that some investors are indeed not rational, hence secondly, if
irrational investors are erratic with their trading activities then they must cancel one another
out. Therefore this would not have an effect on the potency that security prices replicate all
available information on the market. Ultimately, if investors act irrationally in homogeneous
ways then rational arbitrageurs who dominate the markets, will eliminate these anomalies
(Shleifer 2000). Albeit, the impact that irrational investors would have on the financial
markets, would therefore be of insignificance and evanescent.
2.2 Theoretical and empirical challenges to efficient market hypothesis
Within conventional behavioural economics, it is predominantly assumed that economic
marketers are fully informed, uphold Bayesian laws whilst maximising expected utility.
Therefore suggesting that they are fully rational in forming decisions. However many
behavioural economists believe that traditional economic theory is abundant with
imperfections as humans are not always rational. Hayek (1952) argued that it is impossible
for any person to be completely knowledgeable as the limitations of knowledge are
unavoidable leading to discrepancies in decision making, no matter how rational a person
may appear to be. In support of this, Kahneman and Tversky (1973) state that when there is
uncertainty within decision making, this could in fact divert from Bayesian laws and other
behavioural probability theories.
Therefore this now questions the ability and effectiveness of EMH to handle anomalies found
inside the financial system. The trust in this theory has indeed been eroded by anomalies such
as the dot com bubble of 2000s, financial crash of 2008, the calendar and weather effect,
which have contributed to high volatility in stock markets. According to EMH, these anomalies
should not be possible. Many empirical findings which are not consistent with the EMH have
in recent decades been discovered to explain this. For example research by Kiem et al.,(1983)
looks deeper into the calendar effect and finds that in the month of January, returns of small
sized stock seem to always outperform the market, compared to larger stocks. This in fact
challenges the semi-strong form of the EMH, since under this theory, excess returns should
not occur.
Further empirical findings challenge the efficiency of stock prices. Nicholson and Basu (1977)
found that stock prices which have a high-price-to-earnings ratio tend to be overvalued, and
stocks with low-price-to-earnings ratio tend to be undervalued. Evidently, it can be deduced
that there are other factors, apart from news events which contribute to the value of stocks.
To further support this Culter et al (1991) examined 50 largest stock price movements in one
day within the US markets where he found high levels of volatility which could not be justified
by EMH. The evidence suggested that changes in price could be a contributed factor of
investor sentiment.
In order to effectively explain these anomalies, a relatively new field of behavioural finance
has emerged. This field examines financial events from a broader context, specifically from a
social science viewpoint involving psychology. Behavioural finance has been widely used to
successfully challenge many components of EMH theory (Shiller 2003). It has been suggested
that behavioural finance focuses on two fundamental elements; limits to arbitrage and
psychology (Barberis and Thaler, 2003). The former assumes that real world arbitrage comes
with many risks which results in discrepancies within financial markets remaining
unchallenged. This ultimately contradicts the EMH theory since it relies heavily on
arbitrageurs to eliminate mismatches in stock markets. The latter, psychology, aims to
examine what factors determine the decision making of investors. Essentially, behavioural
finance suggests that investor psychology could offer an explanation for anomalies which are
not understood by EMH. The most dominant factor which is of great interest, and the purpose
of this paper is the tendency of individuals to mimic the actions of others, most prominently
known as herding (De bondt et al, 2008).
2.3 Herd Behaviour in Financial Markets
Banerjee (1992) defines herding as ‘everyone doing what everyone else is doing, even when
their private information suggests something quite different.’ This definition can be used to
describe herding in the context of everyday life. However for this paper, a more suitable
definition by Hwang and Salmon (2004) will be used. They state that herding can be defined
as an event which occurs within financial markets, resulting in investors replicating
eachothers actions, by inferred movements in the market. This is in support of Bikchandani
and Sharma (2001) viewpoint as they described herding as imitation behaviour resulting from
individual factors and often leading to inefficient outcomes for the whole market. It is
believed by Bikhchandani and Sharma (2001) that herding can be negative for the financial
markets. This is due to some investors lacking in knowledge of risk-return issues and may
result in defining other investors actions.
Scholastic literature describes two established types of herding which need to be
distinguished; International and Spurious. According to Bikchandani and Sharma (2001),
intentional herding, is the obvious intent by investors to imitate the behaviour made by other
investors. In this context, investors supress their beliefs and intentionally mimic others
following some sort of market consensus. Usually, this type of herding is also known as
informational cascades, or herding arising from informational differences which increases
volatility in markets and leads to market inefficiencies. Welch (1996) explains the cause of
this being psychological factors, suggesting that investors generally feel secure and safe when
following a crowd. In-fact recent empirical research found this type of herding to be present
in Asian markets, Zhou & Lai (2009) found evidence of intentional herding amongst investors
in Hong-Kong.
Spurious herding on the other hand, is where investors are faced with similar decision
problems and information sets thus taking similar decision choices. Therefore in comparison
to intentional herding, spurious herding leads to an efficient outcome. This type of herding
can occur in equity markets, for instance if interest rates were to rise then stocks would
become less attractive investments. This would lead to investors in the same situation holding
smaller stocks in their portfolio thus they are reacting to commonly known public information
(Bikhchandani and Sharma, 2001).
The remaining sub-sections in this chapter will analyse recent literature and research
conducted to provide evidence of herding in various developing and developed countries. To
validate the literature, the final part of this chapter will aim to review the psychology rational
as to why investors tend to herd in their decision making.
2.4 Investor sentiments for herding
This sub-section aims to answer the research question of why Investors herd within financial
markets and the implications this has on the market as a whole.
Baker and Wurgler (2007), define investor sentiment as ‘a belief about future cashflows and
investments which are not justified by the facts at hand’. According to behavioural
economists, economic agents not only base their investment decisions on information and
facts, but also take into account psychology factors and the opinion of others. For example,
Kahneman and Tversky (1970) introduced the prospect theory which shifted away from
traditional economic decision making theories. Within the prospect theory, it is suggested
that losses cause greater psychological impact than does an equivalent amount of gain. This
can be useful in explaining investor sentiment towards herding, as this suggests that the value
of outcomes depends on gains and losses rather than final wealth states. From this it can be
deduced that investor sentiment stems from emotional reactions rather than changes in the
stock market, which then influences expectations of stock returns (Xu and Green, 2013).
Scharfstein and Stein (1990) propose reputation based theory of why herd behaviour is
prevalent in financial markets. This develops upon the idea that an investor who is unsure of
his own capabilities, will imitate the actions of a well-established high ability investor
regardless of his own private information, in order to not be seen as inadequate although his
actions may be correct. This also suggests that investor sentiment is significantly based upon
emotion, as in this case the investor is worried about how they may be seen if their
information was incorrect. It can also be noted that if many investors in a firm were to do this,
it would lead to market inefficiencies thus influencing expectations of stock returns.
Another explanation of investor sentiment towards herding is overconfidence. Odean (1998)
suggests that overconfidence leads to miss-valuation of private information can lead to
adverse effects in the market such as high volatility. They find that overconfidence of
investors on their own private information rather than public information, leads to an
exaggeration of their private information and in return drives stock prices significantly far
away from their fundamental value. This would also increase trading volume as well as
inducing greater liquidity.
Lastly, Bikchandani and Sharma (2001) suggest compensation concerns being an incentive for
Investors to herd. It may be that investment fund managers are expected to receive
remuneration based on performance relative to other investors. In this case, managers may
exhibit herding; although this might limit the amount of remuneration they gain, it will ensure
them against poor performance and against low remuneration. Gumbel (2015) conducted a
study which supports this theory, as the study found that investment fund managers
exhibited herding to assets with high returns in order to receive a higher remuneration.
To conclude this sub-section, there are many factors which contribute towards investor
sentiment and herding. However it is evident that emotion plays a significant role in the
decision making of investors, which then causes inefficiencies in the fundamental value of
stock prices. Moreover, investors are also concerned on a greater scale about their own
reputation rather than the financial gains involved. It could also be concluded that reputationbased herding is a result of risk-averse investors. The limitations to this however, is that
investor sentiment is relatively difficult to measure since it involves theories of psychological
bias which are difficult to apply and test.
2.5 Previous empirical research
A large number of studies have been conducted to find evidence of herd behaviour in
different markets across the globe amongst investors. This paper is mainly based on the study
conducted by Chang et al (2000) which aims to explore the investment behaviour of investors
in both developing and developed countries namely the US, Hong-Kong, Japan, South Korea
and Taiwan. They employ the method of cross-sectional absolute deviation (CSAD), in order
to measure returns dispersion. With this method they find that returns dispersion is an
increasing function of market return and the relationship between the two is linear. In line
with Bikhchandani and Sharma (2001) the study also finds that intentional herding around
market consensus is prevalent in times of significant market movements. Moreover, they also
found that during times of large fluctuation in stock prices, the return dispersion for the US
and Hong Kong was increasing instead of decreasing. Thus, this suggests that herding was not
prevalent in these markets during that specific period of time. On the contrary, the study
found that in the South Korea and Taiwan markets, herd behaviour was indeed present. The
results from this study supports the expectations of this paper, where herding is most
exhibited in developing markets in comparison to developed markets.
Chiang and Zheng (2009) conducted a similar study where they investigated stock market data
from 18 countries between the years 1988 and 2009. Their findings are consistent with Chang
et al (2000) where herding asymmetry is prevalent in developing Asian markets. However the
interesting factor in their study, is that they found no evidence of herding in Latin American
financial markets which constitutes as a developing country with a less regulated financial
system. Moreover they do not provide any explanation why this may be the case. Equivalently
the study also found that there was no evidence of herding in US stock markets. Furthermore,
the study also found that financial crisis triggered significant amounts of herding in the crisis
country of origin, which then spread the crises to neighbouring countries. Evidently, the
results from this study also support the papers expectation of herding being exhibited the
most during times of financial crisis.
Demirer and Kutan (2006) investigated the stock market in China where they concluded that
there was no evidence of herding although there was high stock return dispersion during large
fluctuations in the market. However, research conducted by Tan et al (2008) contradicts these
results as they found evidence of herding in A-share stocks on the Shanghai and Shenzhen
stock exchange. They also discovered that herding was prevalent during both up and down
market conditions, as well as investors being more active during up market conditions,
increased volatility and increased trading volume.
Similarly Kumar and Bansal (2016) employ CSAD to examine herding in Indian stock markets
during the periods of 2010 to 2015. The study surprisingly found no evidence of herd
behaviour, and further goes on to deny any evidence of herding during extreme market
conditions. Their results suggest that investors in India are inclined to take investment
decisions on their own accord and do not imitate the investment behaviour of other investors.
However the study does not take into account the financial crash period which does indeed
suggests evidence of herding. On the contrary a study conducted by Sumitra and Sidharth
(2013) investigated the period 2003-2008, their results support the fact that herding is
exhibited in Indian stock markets throughout times of intense price movements due to stock
return dispersions decreasing rather than increasing. As expected, the study also found that
herd behaviour was significantly prominent during the financial crisis period.
From a developed market perspective, Khan et al (2011) investigated herding within four
European countries. The results suggested evidence of insignificant amounts of herding in
France and the UK however no herding in Italy and Germany. Evidently, they found that the
German and Italian stock markets correlated with the efficient market hypothesis and as a
result herd behaviour was determined not to be a factor during times of market stress and
fluctuations.
To summarize this literature review it can be concluded that there is extensive research on
herding in the US and Asian markets. Evidence suggests that there is significantly more
herding in developing markets rather than developed markets. However it can also be noted
that the evidence of herding in developing markets can be contradicting. It is also evident,
that herding is more-so likely during times of large price fluctuations and times of market
stress. Although there is much research on India, China, Portugal, US and UK there seems to
be very little research on herding in Russia. Therefore this paper will aim to contribute findings
from the Russian stock market to this phenomenon, and further investigate the differences
between developing and developed markets.
Chapter 3: Methodology
The following chapter will present the approach of this paper, whilst validating the empirical
research which have been used to make this research possible. The paper will also explore
the most effective model of measuring herd behaviour, as well as a discussion on the
limitations on the studies used.
3.1 Countries in question
Due to the nature of this literature based paper, the paper will expand upon various studies
which have focussed on herd behaviour within the UK, US, Portugal, India, China and Russian
financial markets. The main reason these countries have been chosen, is that they show an
extreme contrast in regards to developing and developed markets. UK, US and Portugal were
chosen since they belong in a group with the worlds largest financial and most profound
financial markets where according to the expectations of this study, herding should not be
present however a periodic phenomenon. On the contrary, India, China and Russia are
developing countries which have a less regulated financial system. Therefore according to the
expectations of this study, evidence of herd behaviour should be more omnipresent
compared to that of developed markets. By having this extensive contrast in the sample
countries, it will allow there to be a clearer comparison and measurement of whether herd
behaviour is prevalent more in developed or developing countries. However, it is to be noted,
that the conclusions which are drawn from this paper, are only as robust as the studies which
are used.
The secondary data which is used in the studies acquired to form this paper are from stock
market price equity indices, which are easily accessed on Thomson Reuters datastream and
Bloomberg at the time period ranging from 2000 – 2016. This time-frame has been used as it
takes into account major financial events which could effect the level of herd behaviour
prevalent in the markets. Additionally, this time frame allows for a more up-dated and recent
contribution to existing literature, as there are many studies investigating herding between
1990 – 2000s. These events include but are not limited to the financial crisis of 2008 and US
debt crisis of 2011, as well as macroeconomic news events, which would therefore aid in a
time varying analysis of up, down market patterns.
3.2 Method used to measure herding in supporting studies
The pioneering methods used to detect herd behaviour were proposed by Christie & Huang
(1995) and further developed by Chang et al (2000). From the use of these model, it has been
possible to develop a theoretical framework to examine market-wide herding behaviour and
asymmetric herding during market stress. These have been widely used in recent studies to
measure herd behaviour.
Christie & Huang (1995) use a cross-sectional standard deviation (CSSD) approach, as it
measures dispersion in terms of investor herding since it determines average individual
returns to the market returns. Therefore, when the individual returns differ from market
return, the level of dispersions increase. Ultimately herding is evident when there is a
decrease in dispersions. However, CSSD is somewhat sensitive to outliers and since this
method only picks up herding during periods of extreme market stress, results may not be as
reliable. Furthermore Chang et al (2000) suggest that the CSSD approach is too rigid to
uncover any empirical evidence of herding.
The studies which have been chosen for this paper adopt the approach of cross-sectional
absolute deviation (CSAD). This is a multivariate regression model influenced by Chang et al
(2000) which aims to control for cross-sectional deviation. CSAD is a non-linear approach
which looks into the relation between dispersion and market return. This implies that
dispersions will increase at a less-than proportional rate with the market return. Ultimately
this is seen as the best approach to use within the existing studies for the paper, as in contrast
to CSSD, this method is able to detect herding in normal conditions as well as market stress.
CSAD is expressed in the studies as follows:
R𝑖𝑡 donates the observed stock return of firm i at time t and R𝑚𝑡 is the equally weighted
average market return, and N is the total number of firms in the sample. Chang et al (2000)
suggest that the return dispersion will be a non-linearly increasing function of absolute
market returns. However during herding investors will ignore their own beliefs and follow the
market consensus which will in return, cause the dispersion to increase or decrease at a
decreasing rate.
In order to detect mark-wide herding, all the respected studies have adopted the following
regression model:
2
𝐶𝑆𝐴𝐷𝑡 = 𝑎 + 𝑌1 |𝑟𝑚,𝑡 | + 𝑌2 𝑟𝑚,𝑡
+ 𝜀𝑡
Where 𝑎 denotes the intercept, 𝑌1 denotes the co-efficient for the equally weighted average
market return. 𝑌2 is the co-efficient for squared equally weighted average market return
which will endeavour to show the non-linear relationship between CSAD and the market. In
order for herd behaviour to be prevalent during extreme market stress, 𝑌2 will have to
statistically significant and negative. A positive 𝑌2 will indicate no evidence of herding, and
that dispersion is increasing at an increasing rate, this would be expected during moments of
tranquillity and normal market conditions (Chang et al, 2000).
This paper will examine outcomes from the chosen existing studies, and evaluate their
regression results in a separate descriptive table which will compare against each respective
country chosen for this paper. It is to be noted, this paper will focus specifically on marketwide herding to achieve more consistent outcomes as well as studies which have a large
sample of price indices. From this analysis it will be possible to examine which countries
exhibit herding the most. Additionally it will also allow for the paper to investigate at which
point herding was most prevalent – pre, post or during crisis periods. However, as previously
mentioned the results of this paper will only be as robust as the studies used.
3.3 Limitations
The main limitation of this paper is due to temporal constraints. With the time-frame given it
would have proved difficult to collect all the daily stock returns from the time period of 20002016, and run the statistical models. By conducting new statistical research, there may have
been the possibility of contributing different results which may have not been indicated in
previous studies. However by adopting a literature-based approach to answer the question
at hand, benefits from the ease of access to the secondary data available will still conclude in
efficient and reliable results.
Another limitation of this study is that some existing literature have conflicting results, as
some studies use institutional investors or others look at non-financial markets. Since this
paper expands upon previous studies, this makes the reliability of the results somewhat
inconsistent and strays the focus away from financial markets. In order to overcome this,
results will need to be evaluated carefully and will need to disregard any patterns which do
not correlate with herd behaviour in financial markets.
Furthermore, since there is only one study which focuses on herd behaviour in Russia, it will
be more difficult to validate and compare the extent of herding in its financial market. This is
mainly due to the communist culture in Russia, where information is not widely distributed.
Another limitation which occurs is due to substantial geographical and cultural differences, it
may not be possible to find similarities between Russia and the other set of developing
countries, which would result in a challenging comparison. Therefore the question of the
extent of herding in comparison to other developing countries in the paper, is prevalent in
Russia may not be effectively validated in this paper.
Chapter 4: Results
Chapter 5: Discussion
The chapter will analyse the results from the previous chapter and deduce conclusions as to
why herding is present at the specific market condition, whilst referring back to studies in the
literature review in order to answer the primary research question of this paper, as well as its
supporting objectives.
5.1 Which countries exhibited herding the most?
For the purpose of this paper, in order to answer the question at hand as well as its supporting
objectives, the main factor this analysis will examine is only the 𝑌2 co-efficient as this is what
determines whether herd behaviour is present in the financial markets or not.
The first country in question is Russia. The data for the regression model in chapter 4 was
derived from Indars et al., (2017) study of herding, with evidence from the Moscow stock
exchange. This data takes into account sub-prime crisis, Russian crisis, Crimea annexation and
macroeconomic news announcements for Russian – specific events. The effects of the subprime crisis and Crimea annexation have been combined and stated as ‘Crisis’ in the model.
As shown in the total herding model sample, there are signs of market-wide herding during
periods of financial crisis. This is evidently shown with a negative and statistically significant
𝑌2 with -3.0565. Furthermore, surprisingly the results show that there is little to no evidence
of herding during pre, and post crisis periods, in fact the results in this period of time show
herding patterns similar to developed European markets as reported in a study by Chiang &
Zheng (2010). According to Indars et al., (2017) the overall amount of herding noted in
Russian stock markets is due to investors mimicking in each other during down market
periods. Ultimately, this can be explained by crisis periods. Russia underwent the subprime
crisis and Crimea annexation, at this time, there was a high amount of uncertainty therefore
investors tend to surmise information about the future by following market consensus. This
is consistent with Bikhchandani and Sharma (2001) theory on intentional herding, which can
allow the paper to deduce that Russian investors exhibit intentional herding during periods
of market stress. However, an intriguing finding from this study to note, is that during the
Russian crisis of 2014, there was no evidence of herding. According to the anticipated findings
of this paper, there should in-fact be evidence of herd behaviour prevalent during times of
crisis. The explanation to this provided by Indars et al., (2017) suggests that during this time
there were lower levels of uncertainty, as investors were prepared for this crisis due to the
worlds reaction to Crimea annexation.
In regards to India, there were many conflicting studies which lead to contradicting results.
Kumar and Bansal (2016) found no evidence of herd behaviour as they didn’t take into
account the financial crisis period, additionally this study only looked into institutional
investors and the IT sector. On the other hand another study by Sumitra and Sidharth (2013)
did find evidence of herd behaviour during periods of extreme market stress. However their
data sample was too small, as they only examined periods from 2003-2008. The results
captured in the model above, are derived from Kumar and Sharma (2018) study on the Indian
stock exchange from 2005 – 2016. As can be seen in the model, astonishingly India does not
exhibit herding during the financial crisis period nor pre-crisis however there is slight evidence
of herding during post- crisis period with a negative and statistically 𝑌2 co-efficient -0.0532.
Although, this is extremely weak evidence and suggests that the 2008 financial crisis only had
a mild effect on India. From these results, it can be deduced that Indian investors may indeed
be rational in terms of their investment decisions whereby they follow the EMH, and do not
tend to mimic the behaviour of other investors.
Similar to that of India, with China there were once again conflicting studies which lead to
inconsistent results. The results of the model above are derived from Tung et al (2013). In this
study, the authors compared their results against Taiwan, Japan and Korea and studied
whether the markets influenced one another, leading to a spill over effect. Although this study
may seem a little outdated, the major financial events as well as macroeconomic news events
are accounted for in their results and show consistent outcomes. Referring to the above
model, it is evident to see that herding is present during financial crisis as well as pre and post
period since 𝑌2 co-efficient is statistically significant and negative. From these results, it can
be stated that no matter the market conditions, herd behaviour is somewhat always present.
Therefore it can be said herd behaviour isn’t necessarily correlated to up or down market
days or financial crisis. Although, compared to crisis and pre crisis period, there is a higher coefficient for post-crisis period in China. This implies that after a crisis period, investors tend
to depend more on cumulative information which leads to herding. According to Tung et al.,
(2013) an explanation for herd behaviour prevalent in all market conditions could be due to
investors evaluating the impact of macroeconomic news on their smaller portfolios. During
this time, it is effectively more practical for investors to follow the aggregate market in
adjusting their portfolios, ultimately leading to herd behaviour. It can be noted that this is an
example of spurious herding as proposed by Bikhchandani et al., (2001). Additionally, the
results of this study are consistent of those with Tan et al., (2008), enhancing the reliability of
this outcome.
Braga (2016) examines herding in the Portuguese stock market by analysing data from the
periods 2000 – 2016. These dates are aligned with the period in which this paper is
investigating. The results from the model above, are once again derived using results from
Braga (2016) study. Contrary to the anticipated findings of this paper, the Portuguese stock
markets exhibit herd behaviour in every market condition, this is undoubtedly similar to the
findings of herd behaviour in China analysed in this paper. Although, in periods of pre- and
post-crisis, the evidence of herding is weaker in Portugal compared to that of China. As a
consequence of the results, it can be inferred that the country may face asset mispricing and
increased volatility (Braga, 2016). Firstly examining the crisis period, it is evident to see that
there is a significant degree of herding at -2.96, which means the dispersion is decreasing with
market return. During this point it can be stated that herd behaviour in Portugal increased
due to the Financial crisis of 2008 and European sovereign debt crisis of 2010., causing high
levels of uncertainty being the reason of significant levels of herding in the stock market. With
this evidence, it is therefore possible to confirm that this finding is consistent with Economou
et al (2011), whereby they state that the global financial crisis lead to an increase in the
amount of herding exhibited in the Portuguese stock market. Furthermore, this is also
consistent with this papers anticipated findings as herd behaviour is more intense during
periods of market stress. Another intriguing finding to note from this study, is that herd
behaviour is stronger during periods of positive return. Consequently this can be an
explanation as to why there is herding during all periods of market conditions, as the evidence
suggests that herding is prevalent in both negative and positive market returns. Once again
this is consistent with the findings of China. On the other hand contrary to Economou et al
(2011) where they found herding to be more pronounced when market movements were
negative.
Finally, this paper analyses the effects of herd behaviour upon US and UK financial markets.
The results in the regression table to determine herding have been derived from Galatorias
et al (2015). The authors investigate periods of crisis with significant events such as dotcom
bubble burst, financial crisis of 2008, Asian crisis, pesos crisis and macroeconomic news
announcement. However for the purpose of this study, the Asian crisis and Pesos crisis will
not be investigated as it is not relevant to the time-frame in question. From the regression
table, the paper finds that both the US and UK financial markets exhibit herd behaviour during
crisis periods. However, in terms of the crisis period, we find that the US has much more
significant amounts of herding due to a negative and statistically significant co-efficient 𝑌2 at
-1.8922 compared to the UK which has -0.4922.. Moreover, during pre and post crisis periods,
there is no evidence of herding in the US, although in the post-crisis period the UK shows
weak evidence of herding at -0.040. Galatorias et al (2015) find there to be a spill-over effect
due to the dotcom bubble burst, which can suggest that the UK market may herd around the
US market. Furthermore, the authors found that the UK only tended to herd during the
dotcom crash and not the financial crisis of 2008. Overall, they found that no other factors
influenced herd behaviour, essentially there is only limited evidence prevalent in the UK. A
study on herd behaviour in the UK by Klein (2013) has similar results and states that the
limited amount of herding in the UK market could have a US origin. This is contrary to China,
Russia, US and Portugal in this paper which exhibited herding due to financial crisis and other
crisis-related events as well as macroeconomic news. Evidently it is an interesting and
unexpected finding to note, since the other countries do not indicate evidence of being
influenced to herd around each other. Another finding by the authors suggests that US
investors tend to herd towards market consensus when important macroeconomic
information is released. This is a similar finding to that of China in this paper, and an example
of spurious herding which is consistent with the argument Bikchandani and Sharma (2001),
where investors are following the same information set thus making similar decisions.
5.2 Outcome of discussion
The outcomes analysed by different literature give this paper a varied outcome. Initially, the
anticipated findings of the paper suggested that herding would be omnipresent in China,
Russia and India and a period phenomenon in the developed countries. However, with the
evidence given, it can be concluded that in this mix of countries, China and Portugal have the
most amount of herding present during, post and pre crisis. Additionally, Russia and India
have minimal amounts of herding, with Russia mostly during the crisis period. As expected,
the US and UK whom both have profound financial markets exhibit herd behaviour during
financial crisis. Although, it must be noted that Galatrorias et al (2015) find that the UK only
herd during the dot com bubble burst, and not during any other crisis in which the study has
investigated. Furthermore it is evident that the main type of herding exhibited by investors,
is spurious herding as suggested by Bikchandani and Sharma., (2001). Therefore it cannot be
said which set of countries – developed or developing exhibit herding the most, instead it can
be deduced that the main factor and market period which influences herd behaviour is indeed
times of financial crisis as well as macroeconomic news announcement.
It can also be argued that in the regression model, Russia has predominantly a higher negative
𝑌2 co-efficient compared to the other countries, this is mainly due to the georgraphical and
cultural differences with Russia having a significantly independent financial market. This also
develops a question of whether markets are interlinked, since evidence from Galatroias et al
(2015) suggests that the UK markets tend to herd around the US markets during certain Crisis.
It may be that the Chinese financial markets and Portuguese financial markets also herd
around a specific country, most likely being the US since their herding results show similar
behaviour. This indeed becomes another area of research which deems interesting to
investigate in further research.
Chapter 6: Conclusion
This chapter will summarise this paper, define the contribution of this paper to existing
literature and present additional suggestions for future research.
6.1 Conclusion of literature review and results
The comprehensive literature review revealed that seminal theory of efficient market
hypothesis introduced by Fama., (1965) fails to uphold in real-world circumstances. There are
many anomalies, such as the financial crash of 2008, dot com bubble burst and European
sovereign crisis of 2010 which cannot be explained by the EMH. Thus suggesting that investors
do not behave in a rational manner in terms of decision making, but infact mimic each other’s
actions which leads to herd behaviour (Bickhandani and Sharma., 2001).
The main anticipated findings of this paper suggested that herd behaviour was a prominent
phenomenon in developing financial markets compared to developed financial markets.
However, the results of this paper show that this may not always necessarily be the case.
Evident during periods of market turmoil, there is a significant amount of uncertainty
amongst investors, leading them to herd towards a market consensus. This was prevalent in
Russia and China. Both Portugal and China presented herd behaviour during pre, during and
post crisis periods, whereas India showed weak evidence of herd behaviour post-crisis. Russia
only showed evidence of herd behaviour during the subprime crisis and Crimea annexation
as found by Edgars et al., (2017). The developed markets of UK and US markets showed
minimal amounts of herding during crisis, however a spill over affect from the US market to
the UK was evident (Galatorias et al., 2015). This also implies that herd behaviour isn’t a
significant issue in these developed countries. These results evidently contradict the
prediction that developed markets are efficient and free from herding.
In regards to investor sentiment, there are many factors which influence herding, such as
compensation concerns, overconfidence and reputation concerns. Mainly emotion tends to
play a significant role, as suggested by Scharfstein and Stein (1990) investors are concerned
about their reputation, and will tend to imitate the actions of investors in a higher position in
order to not be seen as inadequate. The result of investor sentiments leads to market
inefficiencies and drives price levels away from the stocks actual fundamental value.
6.2 Contribution
This paper is aimed to contribute to existing literature by highlighting on herd behaviour in a
set of unconventional developing and developed financial markets – which usually would not
be compared. It is evident that there is a growing emphasis on the significance of developing
markets and their part in global financial markets, therefore it is insightful to include them in
this analysis. Additionally, existing literature regarding Russian financial markets is weak,
therefore including Russia in the set of countries results to an interesting outcome.
Furthermore, the paper also highlights the possible factors which cause investors to herd as
this is not investigated in many of the studies reviewed in this paper.
6.3 Suggestions for future research
The previous studies in this paper, have aimed their focus on the factor of equally-weighted
market returns as a variable to measure herd behaviour. Although, a consideration to
implement more robust variables such as bid-ask spreads or turn over volume may prove to
be suitable for such an application. This data is similar to stock returns in terms that it can be
easily accessible on the Bloomberg terminal or Thomson Reuters DataStream. With these new
variables, it would be possible to include more countries in the analysis which will allow the
paper to expatiate on the initial idea that herd behaviour should have a greater propensity in
developing markets than developed ones. This extended study would reveal an interesting
intuition into individual market attributes amongst stock markets in various regions. Another
perspective as previously stated in the discussion, could be to analyse whether markets herd
around each other. This approach could induce new insights into the extent of herding and
contribute to the study of investor sentiment. Furthermore this could reveal if one country is
a financial market leader which other countries herd around which would further suggest a
spill over effect. Evidently, herd behaviour is an intriguing aspect of human behaviour, which
is useful for behavioural economist and those investigating into behavioural financial,
although it does require further research.
Acknowledgements
Firstly, the author would like to thank her supervisor Charlotte Carruthers for
her deep insight, and support provided for the dissertation. I’d also like to thank
my father, Aslam Bhatti for his financial support through out my time at
University, and my mother Majda Bhatti for her emotional support.
References
Avery, C. & Zemsky, P. (1998), "Multidimensional uncertainty and herd behavior in financial
markets", The American Economic Review, Vol. 88 No.4, pp. 724–748. 2.
Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of
the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook
of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128 Elsevier.
Banerjee, A. (1992), “A simple model of herd behavior”, Quarterly Journal of Economics,
Vol. 107 No. 3, pp. 797–818. 3.
Baker, Malcolm, and Jeffrey Wurgler. 2007. "Investor Sentiment in the Stock
Market." Journal of Economic Perspectives, 21 (2): 129-152.
Basu, S (1977), ‘The Investment Performance of Common Stocks in relation to their PriceEarnings Ratios’ Journal of Finance, Vol. 32, No. 3, pp. 663-82.
BRAGA, André Barreto das Neves de Almeida (2016) - Herd behaviour and market efficiency:
evidence from the portuguese stock exchange [Em linha]. Lisboa: ISCTE-IUL, 2016.
Dissertação de mestrado. [Consult. Dia Mês Ano] Disponível em
www:<http://hdl.handle.net/10071/13598>.
Bikhchandani, S. & Sharma, S. (2000), "Herd behavior in financial markets", IMF Staff Papers,
Vol. 47 No. 3, pp. 279-310. 4.
Chang, E.C., Cheng, J.W. & Khorana, A. (2000), "An examination of herd behavior in equity
markets: an international perspective", Journal of Banking & Finance, Vol. 24 No. 10, pp.
1651-1679. 5.
Chiang, T.C. & Zheng, D. (2010), "An empirical analysis of herd behavior in global stock
markets", Journal of Banking & Finance, Vol. 34 No. 8, pp. 1911-1921. 7.
Christie, W.G., & Huang, R.D. (1995), "Following the pied piper: do individual returns herd
around the 60Global Journal of Management and Business Research Volume XVI Issue V
Version I Year ( ) 2016 ©
Cutler, D., Poterba, J., Summers, L. (1991), ‘What Moves Stock Prices?’, Journal of Portfolio
Management, 15: 4–12.
Demirer, R., Kutan, A. 2006.Does herding behavior exist in Chinese stock markets?, Journal
of International Financial Markets, Institutions and Money 16, 123-142.
De Bondt, W., Thaler, R. 1985. Does the stock market overreact?, Journal of Finance 40, 793805.
Fama, E. F. (1965). The Behaviour of Stock-Market Prices. Journal of Business, 38(1), 34- 105.
Fisher, D. F. and Monty, R. A. and Glucksberg, S. (1969). Visual confusion matrices: Fact or
artifact? The Journal of Psychology, 71, 111-125.
Galariotis, E. C., Rong, W., & Spyrou, S. I. (2015). Herding on fundamental information: A
comparative study. Journal of Banking & Finance, 50, 589-598
Gümbel, A. (2005). Herding in delegated portfolio management: When is comparative
performance information desirable? European Economic Review, 49(3), 599-626.
Haigh, M. S., & List, J. A. (2005). Do Professional Traders Exhibit Myopic Loss Aversion? An
Experimental Analysis. The Journal of Finance, 60(1).
Hayek, F.A. (1952). The sensory order: Inquiry into the foundations of theoretical
psychology. Chicago: Chicago University Press.
Hwang, S., & Salmon, M. (2004). Market Stress and Herding. Journal of Empirical Finance,
11(4), 585–616. 1945-1964 .
Indārs, E., Savin, A. and Lublóy, Á. (2019). Herding behaviour in an emerging market:
Evidence from the Moscow Exchange. Emerging Markets Review, 38, pp.468-487.
Keynes, M. J. (1936). General Theory Of Employment , Interest And Money. Norwalk,
Connecticut: The Easton Press, 1-263. Retrieved December 16, 2016 from
http://cas2.umkc.edu/economics/people/facultypages/kregel/courses/econ645/winter2
011/generaltheory.pdf
Khan, H., Hassairi, S. A., & Viviani, J.-L. (2011). Herd Behaviour and Market Stress: The Case
of Four European Countries. International Business Research, 4(3), 53-67.
Kiem, D.B (1983). Size-related anomalies and stock return seasonality: further empirical
evidence. Journal of financial economics, Vol. 12, Issue 1, p. 13-32
Tan, L., Chiang, T. C., Mason, J. R., & Nelling, E. (2008). Herding behaviour in Chinese stock
markets: An examination of A and B shares. Pacific-Basin Finance Journal, 16(1-2),
Tversky, A., Kahneman, D. (1991), "Loss aversion in riskless choice: a reference-dependent
model", The Quarterly Journal of Economics, Vol. 106 No. 4, pp. 1039-1061.
Kumar, S. and Sharma, R. (2018). A test of herding in investment decision: Evidence from
Indian stock exchange. 10(11).
Scharfstein, D.S. and Stein, J.C. (1990) Herd Behavior and Investment. The American
Economic Review, 80, 465-479.
Shiller, Robert, J. 2003. "From Efficient Markets Theory to Behavioral Finance ." Journal of
Economic Perspectives, 17 (1): 83-104.
Shleifer, A., & Summers, L. H. (1990). The Noise Trader Approach to Finance. Journal of
Economic Perspectives, 4(2), 19-33.
Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40(35), pp.603-615.
Odean, Terrance, Are Investors Reluctant to Realize Their Losses? (December 1997).
Available at
SSRN: https://ssrn.com/abstract=94142 or http://dx.doi.org/10.2139/ssrn.94142
Zhou, R. and Lai, R. (2009). Herding and information based trading. Journal of Empirical
Finance, 16(3), pp.388-393.
XU, Y. and GREEN, C. (2012). ASSET PRICING WITH INVESTOR SENTIMENT: EVIDENCE FROM
CHINESE STOCK MARKETS*. The Manchester School, 81(1), pp.1-32.